Smart advisory for distributed and composite testing teams based on production data and analytics

Abstract

An application testing developer system provides a platform for generating real-time suggestions for allocating test cases to testers in a distributed environment based on monitored characteristics from previous testing of a test application. The application testing developer system includes a smart advisory tool that optimizes test case allocation in real-time, adaptively assigns incentives in real-time to test cases for prioritizing testing of certain test cases over others, and monitors and validates testing activities.

What is claimed is: 
     
       1. A method for generating a suggested application testing schedule, the method comprising:
 receiving, through a network interface, an application feature graph specifying test application features for a test application, wherein the application feature graph is configured in a data tree format including a series of associated application nodes corresponding to the test application features available in the test application; 
 receiving, through the network interface, tester attribute information corresponding to a testing resource, wherein the tester attribute information includes test case execution trace data identifying application nodes included in the data tree that correspond to executed test application features verified by a test execution validator engine as being completed by the testing resource; 
 assigning, by a work allocation engine, application feature weight information to the test application features included in the test application feature graph based on default values; 
 generating, by the work allocation engine, a proposed allocation of testing resources to the test application features for assigning testing resources to the test application features of the test application based on the application feature graph, the tester attribute information, and the application feature weight information; 
 assigning, by the work allocation engine, a priority level to each application feature available in the test application; 
 assigning, by the work allocation engine, reward values to each application node included in the application feature graph such that a first application node corresponding to a first application feature assigned a higher priority level is assigned a higher reward value compared to a second application node corresponding to a second application feature assigned a lower priority level; and 
 allocating the reward values to a testing resource for each application node executed by the testing resource. 
 
     
     
       2. The method of  claim 1 , wherein each of the associated application nodes describe a step for executing the respective application feature. 
     
     
       3. The method of  claim 1 , wherein the tester attribute information includes at least one of a testing strength or testing weakness of a testing resource. 
     
     
       4. The method of  claim 1 , further comprising:
 assigning, by the work allocation engine, reward values to each application node included in the application feature graph; and 
 allocating the reward values to a testing resource for each application node executed by the testing resource. 
 
     
     
       5. The method of  claim 4 , further comprising:
 receiving, by the work allocation engine, updated test case execution trace data describing application nodes of the respective application feature that have been executed by the testing resource; and 
 revising, by the work allocation engine, the assignment of reward values to each application node included in the application feature graph based on the updated test case execution trace data. 
 
     
     
       6. The method of  claim 5 , wherein the reward values are assigned to application features, at least in part, based on known defects of the application features. 
     
     
       7. The method of  claim 5 , further comprising:
 assigning, by the work allocation engine, a priority level to each application feature available in the test application; and 
 wherein generating a real-time suggestion comprises:
 assigning higher rated testing resources to application features assigned a high priority level; and 
 assigning lower rated testing resources to application features assigned a low priority level. 
 
 
     
     
       8. A computing device comprising a processor and a memory for generating a suggested application testing schedule, the computing device further comprising:
 a network interface configured to:
 receive, from a database, an application feature graph specifying test application features for a test application, wherein the application feature graph is configured in a data tree format including a series of associated application nodes corresponding to the test application features available in the test application; and 
 receive, from a database, tester attribute information corresponding to a testing resource, wherein the tester attribute information includes test case execution trace data identifying application nodes included in the data tree that correspond to executed test application features verified by a test execution validator engine as being completed by the testing resource; and 
 
 a work allocation engine configured to:
 assign application feature weight information to the test application features included in the test application feature graph based on default values; 
 generate a proposed allocation of testing resources to the test application features for assigning testing resources to the test application features of the test application based on the application feature graph, the tester attribute information, and the application feature weight information; 
 assign a priority level to each application feature available in the test application; 
 assign reward values to each application node included in the application feature graph such that a first application node corresponding to a first application feature assigned a higher priority level is assigned a higher reward value compared to a second application node corresponding to a second application feature assigned a lower priority level; and 
 allocate the reward values to a testing resource for each application node executed by the testing resource. 
 
 
     
     
       9. The computing device of  claim 8 , wherein each of the associated application nodes describe a step for executing the respective application feature. 
     
     
       10. The computing device of  claim 8 , wherein the tester attribute information includes at least one of a testing strength or testing weakness of a testing resource. 
     
     
       11. The computing device of  claim 8 , wherein the work allocation engine if further configured to:
 assign reward values to each application node included in the application feature graph; and 
 allocate the reward values to a testing resource for each application node executed by the testing resource. 
 
     
     
       12. The computing device of  claim 11 , wherein the work allocation engine is further configured to:
 receive updated test case execution trace data describing application nodes of the respective application feature that have been executed by the testing resource; and 
 revise the assignment of reward values to each application node included in the application feature graph based on the updated test case execution trace data. 
 
     
     
       13. The computing device of  claim 12 , wherein the reward values are assigned to application features, at least in part, based on known defects of the application features. 
     
     
       14. The computing device of  claim 8 , wherein the work allocation engine is further configured to:
 assign a priority level to each application feature available in the test application; and 
 wherein the work allocation engine is configured to generate a real-time suggestion by:
 assigning higher rated testing resources to application features assigned a high priority level; and 
 assigning lower rated testing resources to application features assigned a low priority level.